Enhancing Financial Decision Making Using Multi-Objective Financial Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InProceedings{Li:2006:CEC,
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author = "Jin Li and Sope Taiwo",
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title = "Enhancing Financial Decision Making Using
Multi-Objective Financial Genetic Programming",
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booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
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year = "2006",
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editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and
Simon M. Lucas",
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pages = "7935--7942",
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address = "Vancouver",
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month = "16-21 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "0-7803-9487-9",
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URL = "http://www.cs.bham.ac.uk/~jxl/cercialink/web/publication/MOFGP-JinSope.pdf",
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DOI = "doi:10.1109/CEC.2006.1688575",
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size = "8 pages",
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abstract = "a multi-objective genetic programming based financial
forecasting system, MOFGP. MOFGP is built upon our
previous decision-making tool, FGP (Financial Genetic
Programming) [1]-[5]. By taking advantage of the
techniques of multi-objective evolutionary algorithms
(MOEAs), MOFGP enhances FGP in a number of ways.
Firstly, MOFGP is faster in obtaining the same quantity
of diverse forecasting models optimised with respect to
multiple conflicting objectives. This is attributed to
the inherent property of MOEAs, i.e., a set of Pareto
front solutions can be obtained in a single execution
of its algorithm. Secondly, MOFGP is friendlier and
simpler from the user's perspective. It is friendlier
because it eliminates a number of user-supplied
parameters previously required by FGP. Consequently, it
becomes simpler as the user no longer needs to have a
priori domain knowledge required for the proper use of
those parameters. Finally, compared with FGP, which
exploits a canonical single-objective approach to
tackle a multi-criterion financial forecasting problem,
MOFGP demonstrates the above advantages without
seriously sacrificing its forecasting performance,
although it suffers from an inadequate generalisation
capability over the test data in this study. Given its
strengths and weaknesses, MOFGP could be employed as a
useful starting investigative tool for financial
decision making.",
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notes = "Also known as \cite{1688575} NB Dec 2023 IEEE xplor
gives page numbers 2171-2178
WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D",
- }
Genetic Programming entries for
Jin Li
Sope Taiwo
Citations